def __handle_stat(self, stat, id, topic, container_name):
try:
read_dt = parser.parse(stat["read"])
timestamp = int((time.mktime(read_dt.timetuple()) + (read_dt.microsecond / 1000000.0)) * 1000)
memory_usage = float(stat["memory_stats"]["usage"]) / float(stat["memory_stats"]["limit"])
Metric.create(topic=topic, container=container_name, timestamp=timestamp, name="memory", value=memory_usage)
# Calculate CPU usage. The docker API returns the number of cycles consumed
# by the container and the number of cycles consumed by the system. We need
# to take the difference over time and divide them to retrieve the usage
# percentage.
total_usage = float(stat["cpu_stats"]["cpu_usage"]["total_usage"])
system_usage = float(stat["cpu_stats"]["system_cpu_usage"])
if id in self.id_to_cpu:
usage_diff = total_usage - self.id_to_cpu[id]["total"]
system_diff = system_usage - self.id_to_cpu[id]["system"]
if usage_diff >= 0:
usage_pct = usage_diff / system_diff
else:
usage_pct = 0.0
Metric.create(topic=topic, container=container_name, timestamp=timestamp, name="cpu", value=usage_pct)
self.id_to_cpu[id] = {"total": total_usage, "system": system_usage}
except:
# We don't want to kill the stat thread, and we don't really mind
# if some statistics aren't saved properly
pass
def get_metrics(registration_id):
# Collect and return the following metrics:
# - Current number of containers running for this registration
# - Average CPU usage for this registration
# - Average memory usage for this registration
# - Average processing time for this registration
# - Number of events per day for the last week
result = {}
registration = Registration.get(Registration.id == registration_id)
stats = Metric.select(
Metric.name,
fn.AVG(Metric.value).alias('average')
).where(
Metric.topic == registration.topic,
Metric.container == registration.container
).group_by(Metric.name)
# Add to the result object
for stat in stats:
result[stat.name] = stat.average
# Use the beginning of the day as a starting point (for full
# days)
today_dt = datetime.datetime.combine(
datetime.date.today(),
datetime.time.min
)
today = int(time.mktime(today_dt.timetuple()) * 1000)
# Get the starting point (one week ago)
one_week_ago = today - (7 * MILLISECONDS_IN_DAY)
events = Event.select(
# In order to query by date intervals, we need to divide by one day
# worth of milliseconds, round to the nearest whole number, then
# multiply the milliseconds back in. This will create intervals and
# normalize the returned values.
(fn.ROUND(Event.timestamp/MILLISECONDS_IN_DAY) * MILLISECONDS_IN_DAY).alias('date'),
fn.COUNT(Event.id).alias('events')
).where(
Event.topic == registration.topic,
Event.container == registration.container,
Event.timestamp >= one_week_ago
).group_by('date')
# Add to the result object
result['events'] = {}
for event in events:
result['events'][int(event.date)] = event.events
avg_duration = Event.select(fn.AVG(Event.duration)).where(
Event.topic == registration.topic,
Event.container == registration.container
).scalar()
result['duration'] = avg_duration
result['containers'] = docker_agent.container_count(registration.topic, registration.container)
return result
def get_metrics(registration_id):
# Collect and return the following metrics:
# - Current number of containers running for this registration
# - Average CPU usage for this registration
# - Average memory usage for this registration
# - Average processing time for this registration
# - Number of events per day for the last week
result = {}
registration = Registration.get(Registration.id == registration_id)
stats = Metric.select(
Metric.name,
fn.AVG(Metric.value).alias('average')).where(
Metric.topic == registration.topic,
Metric.container == registration.container).group_by(Metric.name)
# Add to the result object
for stat in stats:
result[stat.name] = stat.average
# Use the beginning of the day as a starting point (for full
# days)
today_dt = datetime.datetime.combine(datetime.date.today(),
datetime.time.min)
today = int(time.mktime(today_dt.timetuple()) * 1000)
# Get the starting point (one week ago)
one_week_ago = today - (7 * MILLISECONDS_IN_DAY)
events = Event.select(
# In order to query by date intervals, we need to divide by one day
# worth of milliseconds, round to the nearest whole number, then
# multiply the milliseconds back in. This will create intervals and
# normalize the returned values.
(fn.ROUND(Event.timestamp / MILLISECONDS_IN_DAY) * MILLISECONDS_IN_DAY
).alias('date'),
fn.COUNT(Event.id).alias('events')).where(
Event.topic == registration.topic,
Event.container == registration.container,
Event.timestamp >= one_week_ago).group_by('date')
# Add to the result object
result['events'] = {}
for event in events:
result['events'][int(event.date)] = event.events
avg_duration = Event.select(fn.AVG(Event.duration)).where(
Event.topic == registration.topic,
Event.container == registration.container).scalar()
result['duration'] = avg_duration
result['containers'] = docker_agent.container_count(
registration.topic, registration.container)
return result
def __handle_stat(self, stat, id, topic, container_name):
try:
read_dt = parser.parse(stat['read'])
timestamp = int((time.mktime(read_dt.timetuple()) +
(read_dt.microsecond / 1000000.0)) * 1000)
memory_usage = float(stat['memory_stats']['usage']) / float(
stat['memory_stats']['limit'])
Metric.create(topic=topic,
container=container_name,
timestamp=timestamp,
name='memory',
value=memory_usage)
# Calculate CPU usage. The docker API returns the number of cycles consumed
# by the container and the number of cycles consumed by the system. We need
# to take the difference over time and divide them to retrieve the usage
# percentage.
total_usage = float(stat['cpu_stats']['cpu_usage']['total_usage'])
system_usage = float(stat['cpu_stats']['system_cpu_usage'])
if id in self.id_to_cpu:
usage_diff = total_usage - self.id_to_cpu[id]['total']
system_diff = system_usage - self.id_to_cpu[id]['system']
if usage_diff >= 0:
usage_pct = usage_diff / system_diff
else:
usage_pct = 0.0
Metric.create(topic=topic,
container=container_name,
timestamp=timestamp,
name='cpu',
value=usage_pct)
self.id_to_cpu[id] = {'total': total_usage, 'system': system_usage}
except:
# We don't want to kill the stat thread, and we don't really mind
# if some statistics aren't saved properly
pass
